Online retailers take into account history clicks when forecasting product market demand. Assuming that online retailers can forecast the market demand accurately, this study focuses on a supply chain composed of one online retailer together with multiple suppliers. When an online retailer determines an order quantity, the amount of maximum inventory is decided on the basis of “current demand plus safety stock,” rather than “average of historical demand plus safety stock.” This study investigates the influence that market demand information sharing among online retailers has on both the bullwhip effect on the supply chain and on a supplier’s inventory level. The results prove that market demand information sharing between online retailers can reduce the bullwhip effect on the supply chain, and can also reduce a supplier’s inventory level. In addition, the demand correlation coefficient in a continuous cycle has the most significant impact on influencing the value of information sharing.
Information sharing Online retailer Bullwhip effect Inventory level
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This paper has received funding from the National Natural Science Foundation, China (71573067, 71271062). And thanks Jinhu Huang for his help in the construction of mathematical model.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Agrawal S, Sengupta RN, Shanker K (2009) Impact of information sharing and lead time on bullwhip effect and on-hand inventory. Eur J Oper Res 192(192):576–593MathSciNetCrossRefzbMATHGoogle Scholar
Babai MZ, Boylan JE, Syntetos AA et al (2015) Reduction of the value of information sharing as demand becomes strongly auto-correlated. Int J Prod Econ 181:130–135CrossRefGoogle Scholar
Chen L, Lee HL (2009) Information sharing and order variability control under a generalized demand model. Manage Sci 55(5):781–797CrossRefzbMATHGoogle Scholar
Cui N, Cui Q, Wang T (2013) Effect of contextual factors of online retailing on customer patronage intentions. J Manag Sci China 16(1):42–58 (in Chinese)Google Scholar
Darke PR, Brady MK, Benedicktus RL et al (2016) Feeling close from afar: the role of psychological distance in offsetting distrust in unfamiliar online retailers. J Retail 92(3):287–299CrossRefGoogle Scholar
Ganesh M, Raghunathan S, Rajendran C (2008) The value of information sharing in a multi-product supply chain with product substitution. IIE Trans 40(12):1124–1140CrossRefGoogle Scholar
Huang YS, Hung JS, Ho JW (2017) A study on information sharing for supply chains with multiple suppliers. Comput Ind Eng 104:114–123CrossRefGoogle Scholar
Huber J, Gossmann A, Stuckenschmidt H (2017) Cluster-based hierarchical demand forecasting for perishable goods. Expert Syst Appl 76:140–151CrossRefGoogle Scholar
Iida T (2015) Benefits of lead-time information and of its combination with demand forecast information. Int J Prod Econ 163:146–156CrossRefGoogle Scholar
Iyer G, Narasimhan C, Niraj R (2007) Information and inventory in distribution channels. Manage Sci 53(10):1551–1561CrossRefzbMATHGoogle Scholar
Ketzenberg ME, Rosenzweig ED, Marucheck AE et al (2007) A framework for the value of information in inventory replenishment. Eur J Oper Res 182(3):1230–1250CrossRefzbMATHGoogle Scholar
Khan M, Hussain M, Saber HM (2016) Information sharing in a sustainable supply chain. Int J Prod Econ 181:208–214CrossRefGoogle Scholar
Khosroshahi H, Husseini SMM, Marjani MR (2016) The bullwhip effect in a 3-stage supply chain considering multiple retailers using a moving average method for demand forecasting. Appl Math Model 40(21–22):8934–8951MathSciNetCrossRefGoogle Scholar
Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43:546–558CrossRefzbMATHGoogle Scholar
Ma Y, Wang N, Che A et al (2013) The bullwhip effect under different information-sharing settings: a perspective on price-sensitive demand that incorporates price dynamics. Int J Prod Res 51(10):3085–3116CrossRefGoogle Scholar
Menon S, Kahn B (2002) Cross-category effects of induced arousal and pleasure on the internet shopping experience. J Retail 78(1):31–40CrossRefGoogle Scholar
Rached M, Bahroun Z, Campagne JP (2015) Assessing the value of information sharing and its impact on the performance of the various partners in supply chains. Comput Ind Eng 88(22):237–253CrossRefGoogle Scholar
Riquelme IP, Román S, Iacobucci D (2016) Consumers’ perceptions of online and offline retailer deception: a moderated mediation analysis. J Interact Mark 35:16–26CrossRefGoogle Scholar
Rosienkiewicz M, Chlebus E, Detyna J (2017) A hybrid spares demand forecasting method dedicated to mining industry. Appl Math Model 2017:49Google Scholar
Schu M, Morschett D (2017) Foreign market selection of online retailers–a path-dependent perspective on influence factors [DB/CD]. Int Bus Rev 26(4):710–723CrossRefGoogle Scholar
Sun J, Xu L, Liu Y (2014) Optimal purchase quantity of online retailers under returns issue. Manage Sci 6:114–120 (in Chinese)Google Scholar
Vu DH, Muttaqi KM, Agalgaonkar AP et al (2017) Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment. Appl Energy 2017(205):790–801CrossRefGoogle Scholar